לוח משרות דרושים NLP/Machine Learning משרה מלאה, לחיפוש זה נמצאו 108 משרות

We are looking for a Principal MLOps Engineer with a deep focus on ML Platforms and Infrastructure to join our Data & AI group at Cortex Research. Our team is responsible for designing, building, and scaling the foundational MLOps and LLMOps platforms that power both our Data Scientists and Security Researchers. You will architect the high-performance core infrastructure that enables these roles to build, train, and deploy advanced AI systems-ranging from optimized Small Language Models (SLMs) to complex agentic workflows and RAG systems. If you are passionate about building scalable compute platforms and automating the full ML lifecycle to solve complex data and security challenges, we want to hear from you.
Key Responsibilities
Scale Distributed Training: Design and optimize infrastructure for training and fine-tuning LLMs and SLMs, leveraging distributed GPU workloads, efficient clustering, and compute optimization.
Automate the ML Lifecycle: Architect robust, automated pipelines for continuous training (CT) and deployment (CD) of models, ensuring a seamless flow from raw data collection to production environments.
Build Model Infrastructure: Own the serving architecture for LLMs/SLMs, balancing latency, throughput, and GPU utilization under production traffic.
Implement Advanced Monitoring: Establish comprehensive observability systems to monitor live model performance, data drift, and computational metrics, feeding insights back into the automated training loops for continuous improvement.
Collaborative Architecture: Partner closely with data scientists and security researchers to productize complex model architectures and streamline their workflows, while collaborating with our DevOps team to integrate with core cloud infrastructure.

Required AI Engineer & Architect
The Role:
As AI Architect in the ACoE, you are the technical backbone of our internal agentic transformation. You will design and build the reference architectures, standards, and shared infrastructure that enable hundreds of AI agents to run reliably, safely, and at scale - across R&D, Sales, Customer Revenue, HR, Finance, and Marketing.
This is a hands-on, highly visible role. You will split your time between deep technical work (designing agent orchestration patterns, building cross-company automations, evaluating platforms) and enabling others (code reviews, technical mentorship of citizen developers, setting standards). You will report directly to the ACoE Lead and work closely with the CTO, BI, and Platform teams.
Key Responsibilities
Architecture & Standards:
Define and maintain our reference architectures for agent orchestration, tool permissions, memory models, inter-agent communication, and data boundaries
Establish technical standards for agentic development - prompt engineering patterns, evaluation harnesses, testing frameworks, and shared agent templates
Drive tooling standardization across in partnership with the CTO and CFO, and lead the annual vendor/platform review
Build & Enable:
Develop and maintain selected cross-company agentic solutions and automation workflows
Build and maintain the shared infrastructure: monitoring integrations, KPI dashboards, the agent registry, and the ACoE knowledge base
Conduct technical reviews and code reviews for agents before production deployment
Serve as technical SME for citizen developers across all business units - unblocking, guiding, and reviewing their work
Governance Support:
Define and implement the pre-deployment evaluation harness and model card standards
Support the AI Governance Officer (initially the ACoE Lead) with technical input on risk classification, incident response, and rollback planning
Contribute to quarterly ethics audits for high-risk agents
Innovation:
Track the rapidly evolving agentic AI ecosystem and bring relevant insights and tools back
Co-author external technical content (whitepapers, conference presentations) to establish our technical thought leadership.

Are you a master of the AI lifecycle who can thrive at the intersection of deep learning research, cybersecurity and large-scale production systems? Do you possess the rare ability to jump into a complex technical crisis, diagnose a bottleneck in a Small Language Model (SLM), and lead a team to a production-ready solution?
As a Distinguished AI/ML Architect, you will be the premier technical authority across the Cortex AI/ML organization. You will serve as the "force multiplier" for multiple AI & ML teams, ensuring that our AI strategy - from endpoint-deployed DL/ML models to cloud-scale agentic products - is executed with world-class precision. You will operate as the primary technical architect and hands-on leader for our most ambitious and difficult initiatives.
Key Responsibilities
Operate at the forefront of AI and cybersecurity, leveraging vast datasets to design and deploy innovative defense mechanisms.
Provide horizontal technical leadership across endpoint, cloud, and agentic AI teams to ensure architectural excellence.
Oversee the design and deployment of different model architectures to guarantee scalability and high-performance standards.
Spearhead high-difficulty initiatives and new research frontiers, moving them from ambiguity to production.
Resolve complex technical bottlenecks across the stack, optimizing model efficiency and runtime performance.
Align research and engineering efforts to transform advanced models into robust, production-grade products.

We are seeking a Senior NLP/LLM Researcher to conduct advanced research and develop innovative applications focused on protecting and evaluating LLMs & agentic applications. In this role, you will collaborate with a multidisciplinary team of scientists and engineers, working together to address critical challenges. You will be responsible for designing, implementing, and evaluating new algorithms, models, and agents that help safeguard LLMs.

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‍Key Responsibilities:

Create real-time protections: Develop innovative methods to detect and mitigate adversarial attacks on agents and keep them in line with the organization's policy such as avoiding hallucinations, leaking personal data etc.
Develop new and unique evaluation methods: Implement and develop advanced metrics to evaluate agents' performance on multiple fronts.
Research and Development: Conduct research on adversarial attacks, model protections and performance evaluation techniques for NLP models. Explore and develop state-of-the-art methods and propose innovative solutions.
Collaboration: Work closely with other team members, participate in brainstorming sessions, and contribute to the team's success.

Required Researcher
Description
We are a world leader in trading algorithms and trade execution technologies development. Our multi-disciplinary teams have developed a unique and highly successful machine learning algorithmic based HFT platform that delivers excellent results.
Our research is run by an outstanding team of brilliant researchers who love the extreme challenge of beating the markets. We consistently profit in challenging markets that get increasingly efficient.
We have no customers, and no marketing. It is a pure technological and algorithmic challenge. Hence the time-to-market from a research idea until it is used and make profit by our trading machines is very short.
In a world increasingly dominated by learning machines and artificial intelligence, we are especially proud of our humans. Our elite team of exceptional people are the soul of our company, and it is our top priority to provide them with a professionally fulfilling environment that supports healthy work-life balance. Our employees are encouraged to pursue their passions outside of work and we are proud to offer them a variety of opportunities, multiple resources and an agile work environment which promotes their well-being.
Our unique research team is expanding, we are looking for smart and humble team members to join the team and solve complex problems. Our technical challenges include developing new trading strategies, , improving the performance of our algorithms, finding new ways to beat the markets. As an ML researcher you will collaborate with our R&D team to execute live trading strategies. If you are looking to make an impact by putting into practice successful ideas and seeing immediately the outcome, make influence on our financial performance this is a great opportunity.
In this role you will:
Mange and lead independent research applying ML/DL methods to a wide variety of datasets.
Creatively find new trading strategies
Work with other team members to optimize our Algorithms.

We are looking for a Software Engineer specializing in ML Systems Engineering to join our Development department.
While vision and language models have become increasingly commoditized, our proprietary deep learning models are unique, fast-evolving, and deployed in live trading across the worlds most efficient and sophisticated financial markets. Operating in this environment presents distinct scaling challenges and continuous opportunities for optimization. Success in this role requires first-principles thinking and a deep understanding of the engineering trade-offs behind high-performance DL systems.
This is a pivotal role within our engineering organization. You will work closely with researchers and engineers across the company, running deep learning models on massive compute clusters and adapting them for production serving under strict and non-trivial constraints.

we care deeply about performance, clarity, and building tools that feel right. Were a small team of engineers who enjoy solving hard backend problems and working close to the data. If you're into Go, value clean architecture, and love building for other developers - this might be a great fit.

What Youll Do

Build high-performance backend systems in Go
Work on real-time data pipelines and large-scale telemetry infrastructure
Tackle tough challenges in distributed systems, scale, and performance
Collaborate with infrastructure, frontend, and product teams to ship features end to end

Were looking for a highly skilled Infrastructure Engineer to join our team and own the scaling, management, and automation of our platforms distributed environments. If youre excited about building high-scale distributed systems and solving deep DevOps and infrastructure challenges, lets talk.

What Youll Do:

Own and scale infrastructure - Design, build, and optimize the backbone of our observability platform, ensuring seamless deployment across hundreds of distributed environments.
Solve complex scalability challenges - Tackle unique problems in multi-cluster Kubernetes environments, multi-cloud setups, and high-ingestion observability pipelines.
Manage data at scale - Build and optimize configurable data pipelines, ensuring efficient ingestion, storage, and querying of large volumes of observability data with resilience, consistency, and analytical capabilities.
Automate everything - Develop infrastructure as code, improve CI/CD processes, and automate environment provisioning for reliability and efficiency.
Enhance system reliability - Design robust monitoring, alerting, and self-healing mechanisms for a high-scale production environment.
Collaborate cross-functionally - Work closely with backend engineers, product teams, and customers to design scalable, developer-friendly infrastructure.
Adopt and implement cutting-edge technologies - Continuously evaluate and introduce new tools and frameworks to improve scalability, performance, and cost efficiency.
Improve deployment efficiency - Optimize Helm charts, Kubernetes operators, and Terraform configurations to streamline environment creation and lifecycle management.

We are hiring Senior AI / Machine Learning Engineers to compose, build, and operate production AI systems across classical machine learning, computer vision, large language models, and agentic workflows. You will work across the full AI engineering lifecycle, from initial development and evaluation to deployment, observability, and ongoing improvement. This role suits engineers who can switch easily between system architecture and hands-on implementation. It is for those who understand what it takes to make AI systems reliable at production scale.

What youll be doing:

Lead the build and delivery of production AI systems across machine learning, computer vision, LLM, and agentic use cases.

Build AI applications and agents that use tools, complete multi-step workflows, maintain state, and operate safely in production.

Develop evaluation strategies, test suites, quality metrics, and production feedback loops for models and AI applications.

Build scalable architectures covering model serving, APIs, data flows, workflow orchestration, observability, security, and failure recovery.

Build durable, distributed workflows using platforms such as Temporal, Prefect, or comparable technologies.

Deploy, monitor, and continuously improve AI systems for quality, latency, efficiency, reliability, scalability, and cost.

Make informed technical decisions around model selection, inference architecture, context management, structured outputs, tool use, and infrastructure.

Establish effective development, deployment, and validation practices for services, models, workflows, and infrastructure And provide technical leadership through architecture reviews, build decisions, code reviews, mentoring, and engineering guidelines.

Own the intelligence layer - the AI research pipeline that classifies and risk-scores every tool we find. Discovery tells us what software an organization runs; the AI does the hard part: figuring out what each tool actually is, what it can do, how it handles data, and how risky it is. You'll own that research and enrichment system end to end - the LLM-backed agents, the prompts and context that drive them, the evals that keep them honest, and the cost and latency of running them at scale.

This is an engineering role, not a research one. You'll ship production TypeScript, and you'll be measured on the accuracy, cost, and reliability of the intelligence the product depends on.

What you'll work on

The multi-agent researcher system: LLM-backed agents that research each tool across topics like platform, data policy, AI models, and agentic capabilities, and return structured, evidence-backed classifications.

Evals and quality: design eval sets, measure classification accuracy and hallucination, and turn prompt changes into regression-tested, reviewable diffs instead of guesswork.

Grounding and trust: cite evidence, resolve contradictions between AI output and validated data, and drive down hallucination on the fields that matter.

Model routing and cost/latency: choose and route across providers, tune concurrency and caching, and keep the pipeline fast and affordable as volume grows.

Structured outputs, tool/function calling, and the schemas and validation that make model output safe to persist.

Deep observability into the pipeline - spans, traces, and metrics for every model call.

Were hiring an AI Backend Engineering Manager to guide and grow a high-impact ML team driving AI-powered innovation across our B2B SaaS platform. Youll lead the design and delivery of AI solutions while mentoring engineers and setting the technical direction for AI-first development at scale.
This is a leadership role with a balance of hands-on engineering and team management, perfect for someone who thrives on solving technical challenges, inspiring a team, and shaping the future of AI in fintech automation.
What You Will Do
Lead & Mentor: Manage, mentor, and grow a team of AI/ML/Backend engineers, fostering technical excellence and career development.
Set Technical Direction: Define the ML strategy, ensuring best practices in architecture, frameworks, and operationalization.
Build and deploy AI-based solutions: Oversee the development and deployment of GenAI/LLM-powered solutions that address real-world challenges across our products.
Scale & Operationalize: Establish scalable ML infrastructure, CI/CD, observability, and data pipelines for high-availability production systems.
Collaborate Cross-Functionally: Partner with product managers, engineers, and business stakeholders, clearly communicate progress, challenges, and outcomes.

As a research engineer , you'll be at the forefront of building systems to evaluate and secure frontier AI models. You'll work on infrastructure and experiments to assess model capabilities, implement agent frameworks, and develop mitigations for advanced AI systems. Your role will involve creating robust evaluation pipelines, developing security-focused testing frameworks, and building tools that help understand and mitigate risks related to frontier models. Youll have a chance to understand the research context and your codes impact and contribute as a meaningful part of a growing team.

Representative projects:

Building a tool to continuously evaluate models and mitigate their risks. From designing the APIs for frontier labs, to building analysis and visualization tools that summarize 10,000+ transcripts into specific conclusions.

Designing and building challenges that measure a models ability to evade discovery, allowing us to see if models can operate on remote systems while avoiding detection by common defensive security tools.

Developing controlled environment frameworks for more secure use of frontier models.

Designing and building agents that improve a models ability to complete complex tasks. Includes many potential avenues, such as incorporating SOTA prompting practices, creating tools for task delegation, and more.

Publishing your research and/or delivering research to our customers.

As a Software Engineer at Irregular, you will take ownership of designing, building, and scaling the production systems that power our evaluation and security platform for frontier AI models.

Your work will focus on creating robust, resilient, and high-performance infrastructure-whether thats distributed pipelines, backend services, or tooling that supports our research teams.

This role is engineering-first with a strong research and cyber component. You will develop systems that must run reliably in production, integrate with external partners, and support large-scale data, experiments, and automated evaluations. Youll drive architectural decisions, lead technical implementations, and shape how our platform evolves.

Representative Responsibilities:

Architecting and scaling production-grade systems and workflows.

Building backend services, APIs, and monitoring tools for large-scale model evaluations.

Designing infrastructure that supports research experiments at scale.

Implementing agent frameworks in production environments

Designing and building challenges that measure a models ability to evade discovery, allowing us to see if models can operate on remote systems while avoiding detection by common defensive security tools.

As an AI Research Team Lead on Research group, you will mentor a talented team of researchers, set the strategic vision for research initiatives, and drive team-wide project goals. Youll partner closely with Product and Engineering leadership to turn open-ended data-security challenges into measurable experiments and shipped features. You will manage the critical balance between research exploration and product delivery, owning the end-to-end lifecycle-from problem framing and data strategy to evaluation, deployment, and ongoing monitoring-helping customers discover, protect, and govern their data at scale.



What Youll Do

Lead and mentor a team of AI researchers, fostering a culture of excellence and innovation while overseeing the end-to-end research lifecycle.
You will act as the technical and strategic lead, defining team priorities, roadmap, and data science methodologies.
Mentor and grow team members through technical guidance, career development, and peer reviews.
Collaborate with cross-functional leadership in Product and Engineering to align research efforts with core business objectives and customer needs.
Manage team performance, resource allocation, and timely project delivery within an agile environment.
Develop, evaluate, and maintain deep learning and NLP solutions to enhance Cyeras core capabilities in sensitive data classification.
Design and architect production-grade agentic workflows. Establish rigorous evaluation pipelines to benchmark agent accuracy, latency, and cost, ensuring reliable, scalable solutions for real-world customer problems
Innovation and creative thinking are the keys! Implementing ML models to the entire research process - clustering, text extraction, document analysis, and tabular data classification.
Join a full stack AI group, including research engineering, MLEs, data operations, and security researchers. You will accelerate the path from research to production, ensuring results are both quick and precise.

As an AI Researcher on Research group, youll research, develop and productionize LLMs powered applications and AI-agents. Youll partner closely with Product and Engineering to turn open-ended data-security challenges into measurable experiments and shipped features. Youll own the end-to-end lifecycle - from problem framing and data strategy to evaluation, deployment, and ongoing monitoring - helping customers discover, protect, and govern their data at scale.



What Youll Do

Responsible for the end-to-end research process. This includes identifying problems, preparing data, tuning and developing models, deploying to production, and analyzing outcomes.
You will be a hands-on domain leader, laying the foundations of our data science workflows and algorithms. This is an excellent opportunity to work with endless amounts of data and creatively generate insights that will increase the ability to classify tons of data.
Develop, evaluate, and maintain deep learning and NLP solutions to enhance core capabilities in sensitive data classification.
Design and architect production-grade agentic workflows. Establish rigorous evaluation pipelines to benchmark agent accuracy, latency, and cost, ensuring reliable, scalable solutions for real-world customer problems
Innovation and creative thinking are the keys! Implementing ML models to the entire research process - clustering, text extraction, document analysis, and tabular data classification.
Join a full stack AI group, including research engineering, MLEs, data operations, and security researchers. You will accelerate the path from research to production, ensuring results are both quick and precise.